CN102590749B - A kind of design method for cell charge state prediction kernel - Google Patents
A kind of design method for cell charge state prediction kernel Download PDFInfo
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- CN102590749B CN102590749B CN201110008788.7A CN201110008788A CN102590749B CN 102590749 B CN102590749 B CN 102590749B CN 201110008788 A CN201110008788 A CN 201110008788A CN 102590749 B CN102590749 B CN 102590749B
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Abstract
The present invention proposes a kind of design method for cell charge state prediction kernel, and this kernel can predict battery charge state value according to the voltage of input, charge-discharge magnification, charge and discharge mode.The present invention comprises: 1, charge and discharge mode identification module；2, multiplying power size identification module；3, the identification module of voltage swing；4, state-of-charge prediction kernel module.The present invention is the characteristic point by selecting part reflection battery charge state change, sets up battery static characteristic and discharge and recharge dynamic Characteristic Data storehouse, develops by the characteristic point in this data base and cell charge state prediction kernel.Its feature is, the input in this cell charge state prediction kernel inputs information data such as () charge and discharge mode, multiplying power and voltages of any feature point can dope the battery charge state value at characteristic point.
Description
Technical field
The invention belongs to battery management system technical field, particularly relate to electric automobile power battery management system.
Background technology
The energy and environment are closely related with the survival and development of human society, along with the whole world is to the energy-conservation and enhancing of environmental consciousness, in recent years
Coming, it is all that development new-energy automobile is sustainable as raising inter-industry competition ability, holding economic society that World Main Auto produces state
The Major Strategic behave of development, hybrid vehicle and electric automobile will become the development trend of future automobile industry.Electrokinetic cell
Group is the critical component of hybrid vehicle, and its performance will directly affect the overall permanence of car load, need battery management system to it
Monitoring in real time, battery charge state is again the key factor judging battery performance simultaneously, the most accurately and reliably obtains
Battery charge state is one of the main task of battery management system.Batteries management system can monitor the total of set of cells in real time
Electric current, total voltage, single Battery pack module voltage, electric current and it needs to prediction set of cells state-of-charge, transmitting data in real time is given whole
Vehicle control.The safety of hybrid-power battery group is played an important role by batteries management system, then real-time and accurate
The battery charge state gone out under free position has great importance.
The associated description to functional verification platform of battery management system is had, if practical New Name is battery charge in existing patent
Condition detection method (patent No. ZL200810136561.9) this patent is on the basis of ampere-hour meter is mensuration, passes through store-memory
Chip obtains electricity after the voltage under the different cycle charge-discharge number of times under different temperatures-state-of-charge curve normalized
Discharge and recharge normalized curve battery charge state value SOC in pond carries out calibrating the detection realizing battery charge state.Utility model title
For the patent (patent No. ZL200710143406.5) of the assay method of battery charge state, this patent uses SOC theoretical value meter
Calculate formula and calculate battery charge state SOC theoretical value SOC ↓ [the managing] in multiple moment；Record the battery charge in the plurality of moment
State SOC actual value SOC ↓ [in fact]；It is calculated for expressing SOC ↓ [managing] and the difference of SOC ↓ [in fact] by method of least square
Correction function Φ (t) of the relation between value and the plurality of moment, thus obtain more accurate battery charge state SOC value,
Realize the mensuration of battery charge state.Compared with existing patent, in a kind of cell charge state prediction described in this patent
Core method for designing can be fully achieved battery management system off-line state mode of operation, needs to use instrument to detect in above-mentioned patent
Battery SOC under various modes, adds the complexity of work, has only to select partial reaction battery in this patent
The characteristic point of state-of-charge change, develops to it, in the another advantage of this patent is this cell charge state prediction simultaneously
Core has the strongest adaptivity, again can develop according to the change of external environment condition, the evolved structure that must make new advances, to adapt to
The change of current environment, thus the change of SOC when revising battery charging and discharging.
Summary of the invention
The present invention is a kind of design method for cell charge state prediction kernel that accurately prediction power battery charged state designs, thus
The problem solving to detect the battery charge state under each state in real time.
The power battery charged state prediction core design method that the present invention proposes is by pattern recognition module, multiplying power identification module, voltage
Identification module and inner core module composition.
Described pattern recognition module receives the characteristic point information of input, it is judged that the battery charge state at characteristic point to be predicted is
Under discharge mode or under charge mode, the information after then identifying is transferred to multiplying power identification module and is used for follow-up analysis
Process.
Described multiplying power identification module receives the characteristic point information of input, it is judged that identify the battery charge shape at characteristic point to be predicted
Which multiplying power is state be under, then looks for mating its corresponding inner core module, and changes multiplying power size dress into binary number
Input data as inner core module.
Described voltage identification module receives the characteristic point information of input, it is judged that identify the battery charge shape at characteristic point to be predicted
Which voltage is state be under, and voltage swing dress changes into the corresponding kernel knot that binary number searches out as multiplying power identification module
The input data of structure module.
Described cell charge state prediction kernel module receives the pattern recognition module of characteristic point, multiplying power identification module and voltage and knows
Other module analyzed and processed after data, as the input information of inner core module, by each merit in inner core module
The corresponding input and output connection of energy unit and the information of power function, it was predicted that go out the battery charge state at corresponding characteristic point
Value.
The cell charge state prediction kernel that this kind of method is designed can accurately and reliably dope battery under any state
SOC, it is adaptable to hybrid vehicle and electric automobile, carries out real-time detection, and the present invention to the performance of battery
System is succinct, and cost is relatively low.
Accompanying drawing explanation
Fig. 1 is the population structure block diagram that the present invention proposes.
Fig. 2 is the overview flow chart that the present invention proposes.
Fig. 3 is the design principle figure of the inner core module that the present invention proposes.
Fig. 4 is the flow chart generating inner core module that the present invention proposes.
Fig. 5 is the power function of the individual feature unit of the inner core module that the present invention proposes.
Fig. 6 is the schematic diagram of the pattern recognition module that the present invention proposes.
Fig. 7 is the schematic diagram of the multiplying power identification module that the present invention proposes.
Fig. 8 is the schematic diagram of the voltage identification module that the present invention proposes.
Fig. 9 is that under the discharge mode that the present invention proposes, multiplying power is the logical circuitry of the inner core module of 1C.
Detailed description of the invention
Below in conjunction with accompanying drawing, the invention will be further described.
Fig. 1 is the population structure block diagram of this kind of design method for cell charge state prediction kernel.In the prediction of this power battery charged state
Core design includes: charge and discharge mode identification module, it is judged that the characteristic point identifying battery charge state to be predicted is charge mode
Or discharge mode；Charge-discharge magnification identification module, identifies the multiplying power size of the characteristic point of battery charge state to be predicted,
And it is converted into binary system as judging the required inner core module used of coupling；Charging/discharging voltage identification module, identifies and is wanted pre-
Voltage swing at the characteristic point of the battery charge state surveyed, and it is converted into binary system as inner core module input data；Electricity
Pond state-of-charge prediction kernel module, carrys out predicted characteristics point by the kernel circuitry structure of the functional unit matrix of a fixed size
The SOC at place, the input and output of the inside individual feature unit of functional unit matrix connect the company representing individual feature unit
Connecing state, power function represents the operation function of individual feature unit.
Below in conjunction with accompanying drawing, the module of cell charge state prediction kernel design system is described in detail.
1, structure flow chart is as shown in Figure 2:
Fig. 2 is the overview flow chart of this kind of design method for cell charge state prediction kernel.The battery lotus that left side input is to be predicted
Information at the characteristic point of electricity condition, including: charge and discharge mode, multiplying power size and voltage data.First pass through charge and discharge mode
Identification module, it is judged that characteristic point belongs to any pattern, then which multiplying power judging characteristic point belongs under in such a mode, according to again
Rate information matches corresponding state-of-charge prediction inner core module, finally according to the state-of-charge prediction kernel module found and spy
A magnitude of voltage at levying dopes the battery charge state value at characteristic point to be predicted.
2, the design principle figure of inner core module is as shown in Figure 3:
Fig. 3 is an inner core modular model, and the design basic thought of this kind of inner core module is PLD
Structure bit string, as chromosome, is found by evolution algorithm and is met the chromosome that specific function requires, the optimum chromosome pair of gained
The hardware configuration answered is hardware circuit to be realized.By downloading to compile by the chromosome bit string of these indication circuit structures
In journey logical device, the design function of hardware circuit can be realized.This inner core module generally uses the merit of a fixed size
Energy cell matrix expresses the function of circuit.Fig. 3 is Phenotype and the genotype of this inner core module, and it is by M × N number of solely
Vertical functional unit FE (function element) constitutes a M row, the functional unit matrix of N row, each function list
Unit has 2-to input, and 1-output and power function form.In cartesian genetic program, the 2-of each functional unit input be with
Above the functional unit output of L row is connected, and (L is Connecting quantity, and it represents that the input of current functional unit can connect
Outfan to the functional unit that above L arranges).Meanwhile, each functional unit is by the corresponding function letter of some in Fig. 5
Number goes to process its two input signals.So, internal links and power function by constantly changing each functional unit come
Reconstruct, makes whole system finally develop and an optimum chromosome structure.
3, the flow chart of inner core module is generated as shown in Figure 4:
Fig. 4 is the flow chart generating inner core module, according to the characteristic point selecting part reflection battery charge state to change, sets up
Battery static characteristic and discharge and recharge dynamic Characteristic Data storehouse, the data message of the characteristic point of selection (includes charge and discharge mode, fills
Discharge-rate and voltage) as the importation of inner core logical circuitry, with the electricity corresponding to each characteristic point selected
Pond SOC is as the output part of inner core module, each characteristic point input information and the battery charge shape of its output
State value is relation one to one, then according to the input and output of inner core module, first develops and initial population, by suitable
The assessment of response, the requirement meeting fitness just terminates developing, and otherwise evolution from one generation to the next is gone down, it is achieved the survival of the fittest
Evolutionary process, until develop reach set fitness value, finally give an optimum chromosome string.
4, power function is as shown in Figure 5:
List in Fig. 5 is the power function of each functional unit, represents each merit of state-of-charge prediction inner core function matrix
The energy unit operation function performed by two signals to inputting.
5, pattern recognition module is as shown in Figure 6:
The mensuration of battery charge state exists only under both of which, it may be assumed that charge mode and discharge mode, therefore pattern recognition module
Only need to do one between these two modes and identify the process judged, which pattern is the characteristic point being used for judging to need prediction be under,
We can the charge and discharge mode digitized of input, i.e. set discharge mode as on the occasion of, charge mode is negative value, on the contrary also
Can.Therefore, in state-of-charge prediction inner core module, just have only to a bit and carry out the pattern at judging characteristic point
Identification module, thus, reduce the figure place of the input input data of state-of-charge prediction inner core module.
6, multiplying power identification module is as shown in Figure 7:
Multiplying power for input is different, and the value of the input current of corresponding battery is also the most different, then want in different characteristic point place
The battery charge state of prediction is also the most different, and the value of multiplying power size or electric current is the input of the input as inner core module
Data, the logical operations in inner core module all carrys out computing according to binary system, in order to see the data of characteristic point intuitively
Information, during battery charge state at our predicted characteristics point, the multiplying power size of input or the value of electric current are all metric numbers,
Therefore, multiplying power identification module is when receiving the value of the multiplying power size of characteristic point of input or electric current, and we to convert thereof into
Binary number, if decimal occurs in the multiplying power size of input, we can carry out suitable amplification to the value of multiplying power, then
Carry out the evolution of chromosome again, draw the inner core module of a series of cell charge state prediction kernel, for corresponding feature
The multiplying power of point mates corresponding inner core module, the input and output connection of the individual feature unit of inner core inside modules and merit
Can also can change therewith by function, the battery charge state value of output also can carry out the change of identical multiple therewith.
7, voltage identification module is as shown in Figure 8:
Voltage for input is different, and the battery charge state drawn is also the most different, and voltage swing is defeated as inner core module
When entering the input data of end, battery charge state at our predicted characteristics point, the value of the voltage of input is all metric number,
Therefore, voltage identification module is when receiving the value of the voltage of characteristic point of input, and we to convert thereof into binary number,
If the voltage swing of input is a non-integer, we can carry out suitable amplification to the value of voltage, contaminate
The evolution of colour solid, draws the inner core module of cell charge state prediction kernel, using voltage as the input of inner core module,
By input and output connection and the power function of the individual feature unit of inner core inside modules, it was predicted that go out battery in this characteristic point
Under battery charge state value.
8, inner core circuit diagram is as shown in Figure 9:
Fig. 8 is according to inner core schematic diagram, and selecting multiplying power under discharge mode is the series of features point of 1C, is calculated by evolution
The inner core logical circuitry that method draws.The power function and the input and output that have marked each functional unit in figure connect.
Claims (4)
1. a design method for cell charge state prediction kernel, the method is that the voltage according to characteristic point, charge-discharge magnification, the Parameters Evolution of charge and discharge mode go out a kind of cell charge state prediction kernel, the battery charge state value doped under any characteristic point according to this cell charge state prediction kernel, it is characterised in that:
This kind of design method for cell charge state prediction kernel includes: charge and discharge mode identification module, charge-discharge magnification identification module, charging/discharging voltage identification module and inner core module；
Described charge and discharge mode identification module, two kinds of mode of operations including electrokinetic cell: discharge mode and charge mode, it is as an importation of inner core module, for judging that the characteristic point of the power battery charged state of required prediction belongs under any pattern；
Described multiplying power identification module, including the multiplying power scope residing for the work of electrokinetic cell, multiplying power is as an input parameter of inner core module, for judging that the characteristic point of power battery charged state to be predicted is belonging to any multiplying power, then using the multiplying power size of this characteristic point as the input data of inner core module；
Described voltage identification module, the scope of voltage when normally working including electrokinetic cell, it is as an input parameter of inner core module, it is under any voltage for judging the battery charge state at characteristic point to be predicted, then using the value of this voltage as the input data of inner core module；
What the reception of inner core module was surveyed on described battery charge state top is the input as inner core module of the charge and discharge mode at characteristic point, multiplying power size and magnitude of voltage, dopes the value of battery charge state at a certain characteristic point by the connection within inner core and power function；
Described design method for cell charge state prediction kernel: the structure bit string of PLD as chromosome, found by evolution algorithm and meet the chromosome that specific function requires, the hardware configuration that obtained optimum chromosome is corresponding is hardware circuit to be realized, by downloading in PLD by the chromosome bit string of these indication circuit structures, the design function of hardware circuit can be realized；Core design step is: first, select the characteristic point of part reflection battery charge state change, set up battery static characteristic and discharge and recharge dynamic Characteristic Data storehouse, characteristic point information i.e. charge and discharge mode, charge-discharge magnification and the voltage selected, as the importation of inner core logical circuitry, with the battery charge state value corresponding to each characteristic point selected as the output part of inner core module；Secondly, the battery charge state value of each characteristic point input information and its output is relation one to one, input and output according to kernel module, first developing and initial population, by the assessment of fitness, the requirement meeting fitness terminates developing, otherwise evolution from one generation to the next is gone down, realize the evolutionary process of the survival of the fittest, until the fitness value reaching to set that develops, finally give an optimum chromosome bit string；Finally, optimum chromosome bit string is downloaded in PLD, cell charge state prediction kernel can be drawn.
Design method for cell charge state prediction kernel the most according to claim 1, it is characterised in that:
The state-of-charge of electrokinetic cell is divided into both of which, i.e. state-of-charge under power battery charging pattern and the state-of-charge under discharge mode, therefore this charge and discharge mode identification module has only to judge accurately that the characteristic point of required prediction is under any pattern, therefore, this part has only to use a simple process identifying judgement, i.e. the charge and discharge mode digitized of input, set discharge mode as just, charge mode is negative, have only to a bit and carry out the pattern residing for judging characteristic point, thus reduce the figure place of the input input data of state-of-charge prediction inner core module.
Design method for cell charge state prediction kernel the most according to claim 1, it is characterised in that:
Voltage identification module uses an input calculating process, i.e. voltage is different, the SOC of battery is also different, therefore, first to determine that characteristic point is under any voltage, then using the value of this voltage as the input data of inner core module, thus it is used for predicting the value of the state-of-charge of electrokinetic cell.
Design method for cell charge state prediction kernel the most according to claim 1, it is characterised in that:
What the input of cell charge state prediction kernel module accessed is the judgement information of pattern recognition module, the multiplying power match information of multiplying power identification module and the voltage input data as input, the value of the battery charge state at this characteristic point of the outfan output of inner core module；Two inputs of each single functional unit in inner core module are connected to the outfan of corresponding last layer, outfan is connected to the corresponding input of next layer, select the power function of correspondence simultaneously, which in last layer, go to select by functional unit as effective output according to the number of bits of battery charge state.
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| CN103048626B (en) * | 2012-12-17 | 2015-04-15 | 惠州市亿能电子有限公司 | Method for accurately estimating SOC (State of Charge) |
| CN103267953B (en) * | 2013-06-05 | 2015-09-09 | 安徽安凯汽车股份有限公司 | The evaluation method of a kind of lithium iron phosphate dynamic battery SOC |
| CN107024666B (en) * | 2017-06-22 | 2020-01-10 | 安徽锐能科技有限公司 | Apparatus for estimating battery SOC |
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| US7321220B2 (en) * | 2003-11-20 | 2008-01-22 | Lg Chem, Ltd. | Method for calculating power capability of battery packs using advanced cell model predictive techniques |
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